Assessment of adherence to at-home oral anti-infective therapy among paediatric patients discharged from a Quebec hospital
Bibliographic record
Abstract
OBJECTIVES: Non-adherence to anti-infective therapy contributes to treatment failure and the emergence of bacterial resistance. This study aimed to assess at-home adherence, by paediatric patients, to oral anti-infective (OAI) therapy prescribed for treatment of acute infections and to explore the factors contributing to non-adherence. METHODS: This prospective descriptive study involved French-speaking patients under 16 years of age who were discharged with one or more OAIs prescribed for home administration for a maximum of 30 days. Telephone surveys were used to assess overall adherence, which consisted of primary adherence (patient's ability to procure the medication) and secondary adherence (patient's ability to take the treatment as prescribed). RESULTS: Overall, 51.7% (30/58) of patients were adherent to OAI therapy, with 100% primary adherence (n=69/69) and 51.7% secondary adherence (n=30/58). On average, patients took 98% of the total number of doses prescribed, and non-adherence was related mostly to not following medication administration schedules (63.3% of patients followed the exact schedule). Indeed, the adherence rate for patients taking one or two doses per day was twice the rate for patients taking more than two doses per day (81.8% vs 44.7%, p=0.043). CONCLUSIONS: Half of the paediatric patients treated for acute infections were non-adherent to OAI therapy at home. Interventions are needed to improve this situation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".